用低频信道预测高频波束，模型小99%却保持精度。
Knowledge Distillation for mmWave Beam Prediction Using Sub-6 GHz Channels
- 通过知识蒸馏压缩大模型，设计轻量级学生网络。
- 参数和计算量减少99%，仍达到原模型的波束预测精度。
- 适合高移动性场景的实时毫米波通信系统部署。
在毫米波（mmWave）高移动性环境中，波束成形通常带来巨大的训练开销。尽管先前研究指出可利用低于6 GHz的信道来预测最优毫米波波束，但现有方法依赖于计算和内存需求极高的大型深度学习（DL）模型。本文提出一种基于知识蒸馏（KD）技术的计算高效框架，实现亚6 GHz信道与毫米波波束之间的映射。我们开发了两种基于个体和关系蒸馏策略的紧凑型学生深度学习架构，仅保留少量隐藏层，却能近乎完美地复现大型教师模型的性能。大量仿真表明，所提出的的学生模型在保持教师模型波束预测准确率和频谱效率的同时，将可训练参数和计算复杂度降低了99%。
原文摘要 · Abstract (English)
Beamforming in millimeter-wave (mmWave) high-mobility environments typically incurs substantial training overhead. While prior studies suggest that sub-6 GHz channels can be exploited to predict optimal mmWave beams, existing methods depend on large deep learning (DL) models with prohibitive computational and memory requirements. In this paper, we propose a computationally efficient framework for sub-6 GHz channel-mmWave beam mapping based on the knowledge distillation (KD) technique. We develop two compact student DL architectures based on individual and relational distillation strategies, which retain only a few hidden layers yet closely mimic the performance of large teacher DL models. Extensive simulations demonstrate that the proposed student models achieve the teacher's beam prediction accuracy and spectral efficiency while reducing trainable parameters and computational complexity by 99%.
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